Overview

Dataset statistics

Number of variables21
Number of observations193404
Missing cells555626
Missing cells (%)13.7%
Duplicate rows256
Duplicate rows (%)0.1%
Total size in memory101.6 MiB
Average record size in memory551.1 B

Variable types

NUM11
CAT9
BOOL1

Warnings

Dataset has 256 (0.1%) duplicate rows Duplicates
batsman has a high cardinality: 546 distinct values High cardinality
non_striker has a high cardinality: 541 distinct values High cardinality
bowler has a high cardinality: 437 distinct values High cardinality
player_dismissed has a high cardinality: 507 distinct values High cardinality
fielder has a high cardinality: 507 distinct values High cardinality
total_runs is highly correlated with batsman_runsHigh correlation
batsman_runs is highly correlated with total_runsHigh correlation
player_dismissed has 184357 (95.3%) missing values Missing
dismissal_kind has 184357 (95.3%) missing values Missing
fielder has 186906 (96.6%) missing values Missing
bye_runs is highly skewed (γ1 = 30.46379722) Skewed
noball_runs is highly skewed (γ1 = 34.78726606) Skewed
wide_runs has 187537 (97.0%) zeros Zeros
bye_runs has 192898 (99.7%) zeros Zeros
legbye_runs has 190292 (98.4%) zeros Zeros
noball_runs has 192640 (99.6%) zeros Zeros
batsman_runs has 76062 (39.3%) zeros Zeros
extra_runs has 183154 (94.7%) zeros Zeros
total_runs has 67527 (34.9%) zeros Zeros

Reproduction

Analysis started2020-12-21 07:05:58.719678
Analysis finished2020-12-21 07:07:16.679575
Duration1 minute and 17.96 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

match_id
Real number (ℝ≥0)

Distinct816
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91884.46912
Minimum1
Maximum1237181
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-12-21T12:37:16.904342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1205
median408
Q3613
95-th percentile1216511
Maximum1237181
Range1237180
Interquartile range (IQR)408

Descriptive statistics

Standard deviation318512.9064
Coefficient of variation (CV)3.466449874
Kurtosis8.579006802
Mean91884.46912
Median Absolute Deviation (MAD)204
Skewness3.252235282
Sum1.777082387e+10
Variance1.014504715e+11
MonotocityNot monotonic
2020-12-21T12:37:17.339365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12164984660.2%
 
1262670.1%
 
342630.1%
 
5342620.1%
 
4762620.1%
 
3882610.1%
 
5702590.1%
 
1902590.1%
 
5362580.1%
 
4012580.1%
 
111462570.1%
 
12165172570.1%
 
2112570.1%
 
2572570.1%
 
113392570.1%
 
5162570.1%
 
3672560.1%
 
5672560.1%
 
1532550.1%
 
502550.1%
 
5392550.1%
 
5532550.1%
 
1962550.1%
 
672550.1%
 
4882550.1%
 
Other values (791)18675096.6%
 
ValueCountFrequency (%) 
12480.1%
 
22470.1%
 
32180.1%
 
42470.1%
 
52480.1%
 
62160.1%
 
72540.1%
 
82120.1%
 
92260.1%
 
102390.1%
 
ValueCountFrequency (%) 
12371812350.1%
 
12371802500.1%
 
12371782470.1%
 
12371772470.1%
 
12165472510.1%
 
12165462350.1%
 
12165452300.1%
 
12165442340.1%
 
12165432420.1%
 
12165422160.1%
 

inning
Real number (ℝ≥0)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.483159604
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-12-21T12:37:17.699792image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5019063714
Coefficient of variation (CV)0.3384034801
Kurtosis-1.768127923
Mean1.483159604
Median Absolute Deviation (MAD)0
Skewness0.1105838407
Sum286849
Variance0.2519100057
MonotocityNot monotonic
2020-12-21T12:37:17.991008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
110010951.8%
 
29319948.2%
 
350< 0.1%
 
438< 0.1%
 
58< 0.1%
 
ValueCountFrequency (%) 
110010951.8%
 
29319948.2%
 
350< 0.1%
 
438< 0.1%
 
58< 0.1%
 
ValueCountFrequency (%) 
58< 0.1%
 
438< 0.1%
 
350< 0.1%
 
29319948.2%
 
110010951.8%
 

batting_team
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Mumbai Indians
22619 
Kings XI Punjab
20931 
Royal Challengers Bangalore
20908 
Kolkata Knight Riders
20858 
Chennai Super Kings
19762 
Other values (18)
88326 
ValueCountFrequency (%) 
Mumbai Indians2261911.7%
 
Kings XI Punjab2093110.8%
 
Royal Challengers Bangalore2090810.8%
 
Kolkata Knight Riders2085810.8%
 
Chennai Super Kings1976210.2%
 
Delhi Daredevils187869.7%
 
Rajasthan Royals172928.9%
 
Sunrisers Hyderabad129086.7%
 
Deccan Chargers90344.7%
 
Pune Warriors54432.8%
 
Gujarat Lions35661.8%
 
DC20421.1%
 
SRH19881.0%
 
Delhi Capitals19091.0%
 
Rising Pune Supergiant19001.0%
 
MI18290.9%
 
KXIP17850.9%
 
RCB17720.9%
 
CSK16510.9%
 
KKR16500.9%
 
RR16090.8%
 
Kochi Tuskers Kerala15820.8%
 
Rising Pune Supergiants15800.8%
 
2020-12-21T12:37:18.319402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-21T12:37:18.599248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length16
Mean length16.85362764
Min length2

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a36615411.2%
 
n2677438.2%
 
2665998.2%
 
e2408247.4%
 
i2227386.8%
 
s2124406.5%
 
r1845535.7%
 
l1647545.1%
 
g1193613.7%
 
h1101313.4%
 
u937712.9%
 
K923092.8%
 
o905572.8%
 
R884582.7%
 
d880792.7%
 
t679632.1%
 
C570781.8%
 
b564581.7%
 
y511081.6%
 
D505571.6%
 
I471641.4%
 
j417891.3%
 
S397891.2%
 
P316391.0%
 
p251510.8%
 
Other values (12)1823925.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter248706976.3%
 
Uppercase Letter50589115.5%
 
Space Separator2665998.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
K9230918.2%
 
R8845817.5%
 
C5707811.3%
 
D5055710.0%
 
I471649.3%
 
S397897.9%
 
P316396.3%
 
M244484.8%
 
X227164.5%
 
B226804.5%
 
H148962.9%
 
W54431.1%
 
G35660.7%
 
L35660.7%
 
T15820.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a36615414.7%
 
n26774310.8%
 
e2408249.7%
 
i2227389.0%
 
s2124408.5%
 
r1845537.4%
 
l1647546.6%
 
g1193614.8%
 
h1101314.4%
 
u937713.8%
 
o905573.6%
 
d880793.5%
 
t679632.7%
 
b564582.3%
 
y511082.1%
 
j417891.7%
 
p251511.0%
 
m226190.9%
 
k224400.9%
 
c196500.8%
 
v187860.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
266599100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin299296091.8%
 
Common2665998.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a36615412.2%
 
n2677438.9%
 
e2408248.0%
 
i2227387.4%
 
s2124407.1%
 
r1845536.2%
 
l1647545.5%
 
g1193614.0%
 
h1101313.7%
 
u937713.1%
 
K923093.1%
 
o905573.0%
 
R884583.0%
 
d880792.9%
 
t679632.3%
 
C570781.9%
 
b564581.9%
 
y511081.7%
 
D505571.7%
 
I471641.6%
 
j417891.4%
 
S397891.3%
 
P316391.1%
 
p251510.8%
 
M244480.8%
 
Other values (11)1579445.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
266599100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3259559100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a36615411.2%
 
n2677438.2%
 
2665998.2%
 
e2408247.4%
 
i2227386.8%
 
s2124406.5%
 
r1845535.7%
 
l1647545.1%
 
g1193613.7%
 
h1101313.4%
 
u937712.9%
 
K923092.8%
 
o905572.8%
 
R884582.7%
 
d880792.7%
 
t679632.1%
 
C570781.8%
 
b564581.7%
 
y511081.6%
 
D505571.6%
 
I471641.4%
 
j417891.3%
 
S397891.2%
 
P316391.0%
 
p251510.8%
 
Other values (12)1823925.6%
 

bowling_team
Categorical

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Mumbai Indians
22517 
Royal Challengers Bangalore
21236 
Kolkata Knight Riders
20940 
Kings XI Punjab
20782 
Chennai Super Kings
19556 
Other values (18)
88373 
ValueCountFrequency (%) 
Mumbai Indians2251711.6%
 
Royal Challengers Bangalore2123611.0%
 
Kolkata Knight Riders2094010.8%
 
Kings XI Punjab2078210.7%
 
Chennai Super Kings1955610.1%
 
Delhi Daredevils187259.7%
 
Rajasthan Royals173829.0%
 
Sunrisers Hyderabad127796.6%
 
Deccan Chargers90394.7%
 
Pune Warriors54572.8%
 
Gujarat Lions35451.8%
 
SRH19991.0%
 
DC19961.0%
 
Delhi Capitals19631.0%
 
Rising Pune Supergiant19281.0%
 
MI18991.0%
 
RCB17590.9%
 
KXIP17530.9%
 
RR16880.9%
 
CSK16190.8%
 
Rising Pune Supergiants16150.8%
 
Kochi Tuskers Kerala16140.8%
 
KKR16130.8%
 
2020-12-21T12:37:18.929335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-21T12:37:19.239231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length16
Mean length16.87265517
Min length2

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a36732911.3%
 
n2675098.2%
 
2667498.2%
 
e2413057.4%
 
i2222086.8%
 
s2124686.5%
 
r1847955.7%
 
l1662565.1%
 
g1198753.7%
 
h1104553.4%
 
u933362.9%
 
K920442.8%
 
o914102.8%
 
R892302.7%
 
d877402.7%
 
t683132.1%
 
C571681.8%
 
b560781.7%
 
y513971.6%
 
D504481.5%
 
I469511.4%
 
j417091.3%
 
S394961.2%
 
P315351.0%
 
p250620.8%
 
Other values (12)1823735.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter249073376.3%
 
Uppercase Letter50575715.5%
 
Space Separator2667498.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
K9204418.2%
 
R8923017.6%
 
C5716811.3%
 
D5044810.0%
 
I469519.3%
 
S394967.8%
 
P315356.2%
 
M244164.8%
 
B229954.5%
 
X225354.5%
 
H147782.9%
 
W54571.1%
 
G35450.7%
 
L35450.7%
 
T16140.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a36732914.7%
 
n26750910.7%
 
e2413059.7%
 
i2222088.9%
 
s2124688.5%
 
r1847957.4%
 
l1662566.7%
 
g1198754.8%
 
h1104554.4%
 
u933363.7%
 
o914103.7%
 
d877403.5%
 
t683132.7%
 
b560782.3%
 
y513972.1%
 
j417091.7%
 
p250621.0%
 
k225540.9%
 
m225170.9%
 
c196920.8%
 
v187250.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
266749100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin299649091.8%
 
Common2667498.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a36732912.3%
 
n2675098.9%
 
e2413058.1%
 
i2222087.4%
 
s2124687.1%
 
r1847956.2%
 
l1662565.5%
 
g1198754.0%
 
h1104553.7%
 
u933363.1%
 
K920443.1%
 
o914103.1%
 
R892303.0%
 
d877402.9%
 
t683132.3%
 
C571681.9%
 
b560781.9%
 
y513971.7%
 
D504481.7%
 
I469511.6%
 
j417091.4%
 
S394961.3%
 
P315351.1%
 
p250620.8%
 
M244160.8%
 
Other values (11)1579575.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
266749100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3263239100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a36732911.3%
 
n2675098.2%
 
2667498.2%
 
e2413057.4%
 
i2222086.8%
 
s2124686.5%
 
r1847955.7%
 
l1662565.1%
 
g1198753.7%
 
h1104553.4%
 
u933362.9%
 
K920442.8%
 
o914102.8%
 
R892302.7%
 
d877402.7%
 
t683132.1%
 
C571681.8%
 
b560781.7%
 
y513971.6%
 
D504481.5%
 
I469511.4%
 
j417091.3%
 
S394961.2%
 
P315351.0%
 
p250620.8%
 
Other values (12)1823735.6%
 

over
Real number (ℝ≥0)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.16503795
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-12-21T12:37:19.509332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.678812928
Coefficient of variation (CV)0.5586612618
Kurtosis-1.183427499
Mean10.16503795
Median Absolute Deviation (MAD)5
Skewness0.04821941158
Sum1965959
Variance32.24891627
MonotocityNot monotonic
2020-12-21T12:37:19.764515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
1103965.4%
 
2102515.3%
 
3101535.2%
 
4101125.2%
 
5100845.2%
 
6100645.2%
 
7100205.2%
 
899935.2%
 
999585.1%
 
1099145.1%
 
1198545.1%
 
1298225.1%
 
1398035.1%
 
1496985.0%
 
1596235.0%
 
1694594.9%
 
1793454.8%
 
1890614.7%
 
1984974.4%
 
2072973.8%
 
ValueCountFrequency (%) 
1103965.4%
 
2102515.3%
 
3101535.2%
 
4101125.2%
 
5100845.2%
 
6100645.2%
 
7100205.2%
 
899935.2%
 
999585.1%
 
1099145.1%
 
ValueCountFrequency (%) 
2072973.8%
 
1984974.4%
 
1890614.7%
 
1793454.8%
 
1694594.9%
 
1596235.0%
 
1496985.0%
 
1398035.1%
 
1298225.1%
 
1198545.1%
 

ball
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.615638767
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Memory size1.5 MiB
2020-12-21T12:37:20.035361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.807174232
Coefficient of variation (CV)0.4998215664
Kurtosis-1.081527294
Mean3.615638767
Median Absolute Deviation (MAD)2
Skewness0.09665574213
Sum699279
Variance3.265878704
MonotocityNot monotonic
2020-12-21T12:37:20.299387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13137716.2%
 
23127216.2%
 
33118716.1%
 
43112416.1%
 
53101416.0%
 
63091516.0%
 
755152.9%
 
88650.4%
 
91340.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
13137716.2%
 
23127216.2%
 
33118716.1%
 
43112416.1%
 
53101416.0%
 
63091516.0%
 
755152.9%
 
88650.4%
 
91340.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
91340.1%
 
88650.4%
 
755152.9%
 
63091516.0%
 
53101416.0%
 
43112416.1%
 
33118716.1%
 
23127216.2%
 
13137716.2%
 

batsman
Categorical

HIGH CARDINALITY

Distinct546
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
V Kohli
 
4604
S Dhawan
 
4201
RG Sharma
 
4081
SK Raina
 
4044
DA Warner
 
3831
Other values (541)
172643 
ValueCountFrequency (%) 
V Kohli46042.4%
 
S Dhawan42012.2%
 
RG Sharma40812.1%
 
SK Raina40442.1%
 
DA Warner38312.0%
 
RV Uthappa36521.9%
 
G Gambhir35241.8%
 
MS Dhoni34871.8%
 
CH Gayle33731.7%
 
AM Rahane33261.7%
 
AB de Villiers32651.7%
 
KD Karthik30201.6%
 
AT Rayudu29641.5%
 
SR Watson28891.5%
 
MK Pandey27941.4%
 
PA Patel24441.3%
 
YK Pathan23341.2%
 
JH Kallis22911.2%
 
BB McCullum22721.2%
 
Yuvraj Singh22071.1%
 
M Vijay22061.1%
 
KA Pollard21181.1%
 
SR Tendulkar20441.1%
 
KL Rahul20161.0%
 
SV Samson19621.0%
 
Other values (521)11845561.2%
 
2020-12-21T12:37:20.799364image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2020-12-21T12:37:21.259561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length9
Mean length9.317325391
Min length5

Overview of Unicode Properties

Unique unicode characters54
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
20313711.3%
 
a19949511.1%
 
i877744.9%
 
n839114.7%
 
h814544.5%
 
r775924.3%
 
S735814.1%
 
e731964.1%
 
l684873.8%
 
s483182.7%
 
R473672.6%
 
A450742.5%
 
M449892.5%
 
K444152.5%
 
o412602.3%
 
t404792.2%
 
d391062.2%
 
P381292.1%
 
u375402.1%
 
D372802.1%
 
y346961.9%
 
m308321.7%
 
J261841.5%
 
G255321.4%
 
V244131.4%
 
Other values (29)24776713.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter105049358.3%
 
Uppercase Letter54814630.4%
 
Space Separator20313711.3%
 
Dash Punctuation232< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S7358113.4%
 
R473678.6%
 
A450748.2%
 
M449898.2%
 
K444158.1%
 
P381297.0%
 
D372806.8%
 
J261844.8%
 
G255324.7%
 
V244134.5%
 
B217054.0%
 
C214523.9%
 
H200253.7%
 
T157622.9%
 
W118822.2%
 
L110592.0%
 
Y81961.5%
 
N81121.5%
 
E55121.0%
 
F50440.9%
 
U44440.8%
 
I41760.8%
 
O20120.4%
 
Q16000.3%
 
Z184< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
203137100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a19949519.0%
 
i877748.4%
 
n839118.0%
 
h814547.8%
 
r775927.4%
 
e731967.0%
 
l684876.5%
 
s483184.6%
 
o412603.9%
 
t404793.9%
 
d391063.7%
 
u375403.6%
 
y346963.3%
 
m308322.9%
 
w180451.7%
 
g178241.7%
 
k170931.6%
 
p126821.2%
 
j98030.9%
 
v91930.9%
 
c91540.9%
 
b84800.8%
 
x12900.1%
 
f9860.1%
 
q9370.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-232100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin159863988.7%
 
Common20336911.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a19949512.5%
 
i877745.5%
 
n839115.2%
 
h814545.1%
 
r775924.9%
 
S735814.6%
 
e731964.6%
 
l684874.3%
 
s483183.0%
 
R473673.0%
 
A450742.8%
 
M449892.8%
 
K444152.8%
 
o412602.6%
 
t404792.5%
 
d391062.4%
 
P381292.4%
 
u375402.3%
 
D372802.3%
 
y346962.2%
 
m308321.9%
 
J261841.6%
 
G255321.6%
 
V244131.5%
 
B217051.4%
 
Other values (27)22583014.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
20313799.9%
 
-2320.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1802008100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
20313711.3%
 
a19949511.1%
 
i877744.9%
 
n839114.7%
 
h814544.5%
 
r775924.3%
 
S735814.1%
 
e731964.1%
 
l684873.8%
 
s483182.7%
 
R473672.6%
 
A450742.5%
 
M449892.5%
 
K444152.5%
 
o412602.3%
 
t404792.2%
 
d391062.2%
 
P381292.1%
 
u375402.1%
 
D372802.1%
 
y346961.9%
 
m308321.7%
 
J261841.5%
 
G255321.4%
 
V244131.4%
 
Other values (29)24776713.7%
 

non_striker
Categorical

HIGH CARDINALITY

Distinct541
Distinct (%)0.3%
Missing6
Missing (%)< 0.1%
Memory size1.5 MiB
S Dhawan
 
4601
V Kohli
 
4455
SK Raina
 
4173
RG Sharma
 
4132
G Gambhir
 
3740
Other values (536)
172297 
ValueCountFrequency (%) 
S Dhawan46012.4%
 
V Kohli44552.3%
 
SK Raina41732.2%
 
RG Sharma41322.1%
 
G Gambhir37401.9%
 
AM Rahane35731.8%
 
DA Warner35531.8%
 
RV Uthappa35431.8%
 
AB de Villiers32391.7%
 
CH Gayle31931.7%
 
MS Dhoni31591.6%
 
AT Rayudu30891.6%
 
KD Karthik30611.6%
 
MK Pandey29341.5%
 
SR Watson27371.4%
 
PA Patel26081.3%
 
SR Tendulkar24271.3%
 
BB McCullum23561.2%
 
JH Kallis23331.2%
 
M Vijay22961.2%
 
YK Pathan21651.1%
 
KL Rahul20051.0%
 
Yuvraj Singh19851.0%
 
F du Plessis19741.0%
 
SV Samson19691.0%
 
Other values (516)11809861.1%
 
2020-12-21T12:37:21.749215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7 ?
Unique (%)< 0.1%
2020-12-21T12:37:22.089464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length9
Mean length9.318571488
Min length3

Overview of Unicode Properties

Unique unicode characters54
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
20315011.3%
 
a20095911.2%
 
i874854.9%
 
n838354.7%
 
h814004.5%
 
r773964.3%
 
e738854.1%
 
S737914.1%
 
l679813.8%
 
s480472.7%
 
R473832.6%
 
M456742.5%
 
A448562.5%
 
K444332.5%
 
o398172.2%
 
t397312.2%
 
d397132.2%
 
u380062.1%
 
P377752.1%
 
D366962.0%
 
y352532.0%
 
m308231.7%
 
J261951.5%
 
G258831.4%
 
V245711.4%
 
Other values (29)24751113.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter105091158.3%
 
Uppercase Letter54793630.4%
 
Space Separator20315011.3%
 
Dash Punctuation252< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S7379113.5%
 
R473838.6%
 
M456748.3%
 
A448568.2%
 
K444338.1%
 
P377756.9%
 
D366966.7%
 
J261954.8%
 
G258834.7%
 
V245714.5%
 
B216393.9%
 
C211513.9%
 
H201043.7%
 
T164623.0%
 
W114842.1%
 
L109782.0%
 
N79481.5%
 
Y76731.4%
 
E56391.0%
 
F50770.9%
 
I44090.8%
 
U43520.8%
 
O19950.4%
 
Q15450.3%
 
Z206< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
203150100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a20095919.1%
 
i874858.3%
 
n838358.0%
 
h814007.7%
 
r773967.4%
 
e738857.0%
 
l679816.5%
 
s480474.6%
 
o398173.8%
 
t397313.8%
 
d397133.8%
 
u380063.6%
 
y352533.4%
 
m308232.9%
 
w183951.8%
 
g176691.7%
 
k174341.7%
 
p126581.2%
 
j96690.9%
 
c90520.9%
 
v88630.8%
 
b88540.8%
 
x12260.1%
 
q10530.1%
 
f8790.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-252100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin159884788.7%
 
Common20340211.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a20095912.6%
 
i874855.5%
 
n838355.2%
 
h814005.1%
 
r773964.8%
 
e738854.6%
 
S737914.6%
 
l679814.3%
 
s480473.0%
 
R473833.0%
 
M456742.9%
 
A448562.8%
 
K444332.8%
 
o398172.5%
 
t397312.5%
 
d397132.5%
 
u380062.4%
 
P377752.4%
 
D366962.3%
 
y352532.2%
 
m308231.9%
 
J261951.6%
 
G258831.6%
 
V245711.5%
 
B216391.4%
 
Other values (27)22562014.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
20315099.9%
 
-2520.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1802249100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
20315011.3%
 
a20095911.2%
 
i874854.9%
 
n838354.7%
 
h814004.5%
 
r773964.3%
 
e738854.1%
 
S737914.1%
 
l679813.8%
 
s480472.7%
 
R473832.6%
 
M456742.5%
 
A448562.5%
 
K444332.5%
 
o398172.2%
 
t397312.2%
 
d397132.2%
 
u380062.1%
 
P377752.1%
 
D366962.0%
 
y352532.0%
 
m308231.7%
 
J261951.5%
 
G258831.4%
 
V245711.4%
 
Other values (29)24751113.7%
 

bowler
Categorical

HIGH CARDINALITY

Distinct437
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Harbhajan Singh
 
3451
R Ashwin
 
3320
PP Chawla
 
3279
A Mishra
 
3230
SL Malinga
 
2974
Other values (432)
177150 
ValueCountFrequency (%) 
Harbhajan Singh34511.8%
 
R Ashwin33201.7%
 
PP Chawla32791.7%
 
A Mishra32301.7%
 
SL Malinga29741.5%
 
DJ Bravo28391.5%
 
SP Narine28261.5%
 
RA Jadeja27581.4%
 
P Kumar27211.4%
 
B Kumar27071.4%
 
UT Yadav26501.4%
 
DW Steyn22811.2%
 
Z Khan22761.2%
 
R Vinay Kumar21861.1%
 
YS Chahal21751.1%
 
JJ Bumrah21651.1%
 
SR Watson21371.1%
 
IK Pathan21131.1%
 
I Sharma19991.0%
 
A Nehra19741.0%
 
PP Ojha19451.0%
 
AR Patel18941.0%
 
Sandeep Sharma18781.0%
 
RP Singh18741.0%
 
DS Kulkarni18501.0%
 
Other values (412)13190268.2%
 
2020-12-21T12:37:22.479685image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-21T12:37:22.814631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length9
Mean length9.495832558
Min length5

Overview of Unicode Properties

Unique unicode characters56
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a23504412.8%
 
19983710.9%
 
n982145.3%
 
r978075.3%
 
h956735.2%
 
i815324.4%
 
e779484.2%
 
S712213.9%
 
l581033.2%
 
M474372.6%
 
A450602.5%
 
o445732.4%
 
t431272.3%
 
P430872.3%
 
m428742.3%
 
s407552.2%
 
d394362.1%
 
K375772.0%
 
u373852.0%
 
R363082.0%
 
J341391.9%
 
B265511.4%
 
D237281.3%
 
g224171.2%
 
C220121.2%
 
Other values (31)23468712.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter112465161.2%
 
Uppercase Letter51122127.8%
 
Space Separator19983710.9%
 
Dash Punctuation748< 0.1%
 
Open Punctuation25< 0.1%
 
Decimal Number25< 0.1%
 
Close Punctuation25< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S7122113.9%
 
M474379.3%
 
A450608.8%
 
P430878.4%
 
K375777.4%
 
R363087.1%
 
J341396.7%
 
B265515.2%
 
D237284.6%
 
C220124.3%
 
H187073.7%
 
T172993.4%
 
N149862.9%
 
L111622.2%
 
V110692.2%
 
W100262.0%
 
G90361.8%
 
Y89601.8%
 
I72271.4%
 
U55101.1%
 
F28770.6%
 
O26720.5%
 
Z26360.5%
 
E18540.4%
 
Q80< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
199837100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a23504420.9%
 
n982148.7%
 
r978078.7%
 
h956738.5%
 
i815327.2%
 
e779486.9%
 
l581035.2%
 
o445734.0%
 
t431273.8%
 
m428743.8%
 
s407553.6%
 
d394363.5%
 
u373853.3%
 
g224172.0%
 
k198101.8%
 
y158991.4%
 
j140731.3%
 
w140551.2%
 
v137351.2%
 
b104910.9%
 
p89250.8%
 
c60300.5%
 
f23660.2%
 
z19620.2%
 
q18610.2%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-748100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(25100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
225100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)25100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin163587289.1%
 
Common20066010.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a23504414.4%
 
n982146.0%
 
r978076.0%
 
h956735.8%
 
i815325.0%
 
e779484.8%
 
S712214.4%
 
l581033.6%
 
M474372.9%
 
A450602.8%
 
o445732.7%
 
t431272.6%
 
P430872.6%
 
m428742.6%
 
s407552.5%
 
d394362.4%
 
K375772.3%
 
u373852.3%
 
R363082.2%
 
J341392.1%
 
B265511.6%
 
D237281.5%
 
g224171.4%
 
C220121.3%
 
k198101.2%
 
Other values (26)21405413.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
19983799.6%
 
-7480.4%
 
(25< 0.1%
 
225< 0.1%
 
)25< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1836532100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a23504412.8%
 
19983710.9%
 
n982145.3%
 
r978075.3%
 
h956735.2%
 
i815324.4%
 
e779484.2%
 
S712213.9%
 
l581033.2%
 
M474372.6%
 
A450602.5%
 
o445732.4%
 
t431272.3%
 
P430872.3%
 
m428742.3%
 
s407552.2%
 
d394362.1%
 
K375772.0%
 
u373852.0%
 
R363082.0%
 
J341391.9%
 
B265511.4%
 
D237281.3%
 
g224171.2%
 
C220121.2%
 
Other values (31)23468712.8%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
193279 
1
 
125
ValueCountFrequency (%) 
019327999.9%
 
11250.1%
 
2020-12-21T12:37:22.999757image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

wide_runs
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.036488387
Minimum0
Maximum5
Zeros187537
Zeros (%)97.0%
Memory size1.5 MiB
2020-12-21T12:37:23.154507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2473992732
Coefficient of variation (CV)6.780219503
Kurtosis191.7849123
Mean0.036488387
Median Absolute Deviation (MAD)0
Skewness11.58720402
Sum7057
Variance0.06120640038
MonotocityNot monotonic
2020-12-21T12:37:23.389559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
018753797.0%
 
153682.8%
 
22350.1%
 
52110.1%
 
348< 0.1%
 
45< 0.1%
 
ValueCountFrequency (%) 
018753797.0%
 
153682.8%
 
22350.1%
 
348< 0.1%
 
45< 0.1%
 
52110.1%
 
ValueCountFrequency (%) 
52110.1%
 
45< 0.1%
 
348< 0.1%
 
22350.1%
 
153682.8%
 
018753797.0%
 

bye_runs
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004767223015
Minimum0
Maximum4
Zeros192898
Zeros (%)99.7%
Memory size1.5 MiB
2020-12-21T12:37:23.659464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1140485029
Coefficient of variation (CV)23.92346708
Kurtosis1016.235729
Mean0.004767223015
Median Absolute Deviation (MAD)0
Skewness30.46379722
Sum922
Variance0.01300706101
MonotocityNot monotonic
2020-12-21T12:37:23.929584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
019289899.7%
 
13460.2%
 
41270.1%
 
231< 0.1%
 
32< 0.1%
 
ValueCountFrequency (%) 
019289899.7%
 
13460.2%
 
231< 0.1%
 
32< 0.1%
 
41270.1%
 
ValueCountFrequency (%) 
41270.1%
 
32< 0.1%
 
231< 0.1%
 
13460.2%
 
019289899.7%
 

legbye_runs
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02079584704
Minimum0
Maximum5
Zeros190292
Zeros (%)98.4%
Memory size1.5 MiB
2020-12-21T12:37:24.209321image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1936457844
Coefficient of variation (CV)9.31175268
Kurtosis245.4605614
Mean0.02079584704
Median Absolute Deviation (MAD)0
Skewness13.8793515
Sum4022
Variance0.0374986898
MonotocityNot monotonic
2020-12-21T12:37:24.499624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
019029298.4%
 
127031.4%
 
42340.1%
 
21500.1%
 
321< 0.1%
 
54< 0.1%
 
ValueCountFrequency (%) 
019029298.4%
 
127031.4%
 
21500.1%
 
321< 0.1%
 
42340.1%
 
54< 0.1%
 
ValueCountFrequency (%) 
54< 0.1%
 
42340.1%
 
321< 0.1%
 
21500.1%
 
127031.4%
 
019029298.4%
 

noball_runs
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004544890488
Minimum0
Maximum7
Zeros192640
Zeros (%)99.6%
Memory size1.5 MiB
2020-12-21T12:37:24.789342image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.08523291428
Coefficient of variation (CV)18.75356832
Kurtosis1941.740098
Mean0.004544890488
Median Absolute Deviation (MAD)0
Skewness34.78726606
Sum879
Variance0.007264649676
MonotocityNot monotonic
2020-12-21T12:37:25.039611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
019264099.6%
 
17170.4%
 
223< 0.1%
 
514< 0.1%
 
36< 0.1%
 
74< 0.1%
 
ValueCountFrequency (%) 
019264099.6%
 
17170.4%
 
223< 0.1%
 
36< 0.1%
 
514< 0.1%
 
74< 0.1%
 
ValueCountFrequency (%) 
74< 0.1%
 
514< 0.1%
 
36< 0.1%
 
223< 0.1%
 
17170.4%
 
019264099.6%
 

penalty_runs
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
193402 
5
 
2
ValueCountFrequency (%) 
0193402> 99.9%
 
52< 0.1%
 
2020-12-21T12:37:25.459306image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-21T12:37:25.669642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:25.869693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0193402> 99.9%
 
52< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number193404100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0193402> 99.9%
 
52< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common193404100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0193402> 99.9%
 
52< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII193404100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0193402> 99.9%
 
52< 0.1%
 

batsman_runs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.251375359
Minimum0
Maximum7
Zeros76062
Zeros (%)39.3%
Memory size1.5 MiB
2020-12-21T12:37:26.109541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.609800822
Coefficient of variation (CV)1.28642522
Kurtosis1.626350135
Mean1.251375359
Median Absolute Deviation (MAD)1
Skewness1.58054888
Sum242021
Variance2.591458686
MonotocityNot monotonic
2020-12-21T12:37:26.358911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
07606239.3%
 
17321537.9%
 
42199711.4%
 
2124966.5%
 
689064.6%
 
36360.3%
 
581< 0.1%
 
711< 0.1%
 
ValueCountFrequency (%) 
07606239.3%
 
17321537.9%
 
2124966.5%
 
36360.3%
 
42199711.4%
 
581< 0.1%
 
689064.6%
 
711< 0.1%
 
ValueCountFrequency (%) 
711< 0.1%
 
689064.6%
 
581< 0.1%
 
42199711.4%
 
36360.3%
 
2124966.5%
 
17321537.9%
 
07606239.3%
 

extra_runs
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06664805278
Minimum0
Maximum7
Zeros183154
Zeros (%)94.7%
Memory size1.5 MiB
2020-12-21T12:37:26.669624image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3416196934
Coefficient of variation (CV)5.125726547
Kurtosis93.48815611
Mean0.06664805278
Median Absolute Deviation (MAD)0
Skewness8.304922399
Sum12890
Variance0.1167040149
MonotocityNot monotonic
2020-12-21T12:37:26.928454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
018315494.7%
 
191344.7%
 
24380.2%
 
43660.2%
 
52300.1%
 
377< 0.1%
 
75< 0.1%
 
ValueCountFrequency (%) 
018315494.7%
 
191344.7%
 
24380.2%
 
377< 0.1%
 
43660.2%
 
52300.1%
 
75< 0.1%
 
ValueCountFrequency (%) 
75< 0.1%
 
52300.1%
 
43660.2%
 
377< 0.1%
 
24380.2%
 
191344.7%
 
018315494.7%
 

total_runs
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.318023412
Minimum0
Maximum10
Zeros67527
Zeros (%)34.9%
Memory size1.5 MiB
2020-12-21T12:37:27.229562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.60605852
Coefficient of variation (CV)1.218535654
Kurtosis1.631821194
Mean1.318023412
Median Absolute Deviation (MAD)1
Skewness1.555653332
Sum254911
Variance2.57942397
MonotocityNot monotonic
2020-12-21T12:37:27.509696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
17935641.0%
 
06752734.9%
 
42221811.5%
 
2141927.3%
 
688844.6%
 
37480.4%
 
53570.2%
 
864< 0.1%
 
742< 0.1%
 
1016< 0.1%
 
ValueCountFrequency (%) 
06752734.9%
 
17935641.0%
 
2141927.3%
 
37480.4%
 
42221811.5%
 
53570.2%
 
688844.6%
 
742< 0.1%
 
864< 0.1%
 
1016< 0.1%
 
ValueCountFrequency (%) 
1016< 0.1%
 
864< 0.1%
 
742< 0.1%
 
688844.6%
 
53570.2%
 
42221811.5%
 
37480.4%
 
2141927.3%
 
17935641.0%
 
06752734.9%
 

player_dismissed
Categorical

HIGH CARDINALITY
MISSING

Distinct507
Distinct (%)5.6%
Missing184357
Missing (%)95.3%
Memory size1.5 MiB
SK Raina
 
162
RG Sharma
 
159
RV Uthappa
 
155
V Kohli
 
145
S Dhawan
 
143
Other values (502)
8283 
ValueCountFrequency (%) 
SK Raina1620.1%
 
RG Sharma1590.1%
 
RV Uthappa1550.1%
 
V Kohli1450.1%
 
S Dhawan1430.1%
 
KD Karthik1400.1%
 
G Gambhir1360.1%
 
PA Patel1260.1%
 
SR Watson1210.1%
 
AM Rahane1210.1%
 
AT Rayudu1180.1%
 
DA Warner1140.1%
 
AB de Villiers1130.1%
 
CH Gayle1120.1%
 
Yuvraj Singh1110.1%
 
YK Pathan1100.1%
 
MS Dhoni1070.1%
 
BB McCullum1040.1%
 
KA Pollard1010.1%
 
MK Pandey1000.1%
 
V Sehwag990.1%
 
M Vijay990.1%
 
JH Kallis85< 0.1%
 
DR Smith84< 0.1%
 
SV Samson82< 0.1%
 
Other values (482)61003.2%
 
(Missing)18435795.3%
 
2020-12-21T12:37:27.914678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique91 ?
Unique (%)1.0%
2020-12-21T12:37:28.309338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length3
Mean length3.296948357
Min length3

Overview of Unicode Properties

Unique unicode characters54
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n37263158.4%
 
a19398830.4%
 
94811.5%
 
i40440.6%
 
h39680.6%
 
r37170.6%
 
e34120.5%
 
S33580.5%
 
l30390.5%
 
A21830.3%
 
R21760.3%
 
M21700.3%
 
s21100.3%
 
K19960.3%
 
t19820.3%
 
o19190.3%
 
P18710.3%
 
d17810.3%
 
u17310.3%
 
D15870.2%
 
y14730.2%
 
m14460.2%
 
J12740.2%
 
V10970.2%
 
G10860.2%
 
Other values (29)121231.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter60259694.5%
 
Uppercase Letter255434.0%
 
Space Separator94811.5%
 
Dash Punctuation23< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n37263161.8%
 
a19398832.2%
 
i40440.7%
 
h39680.7%
 
r37170.6%
 
e34120.6%
 
l30390.5%
 
s21100.4%
 
t19820.3%
 
o19190.3%
 
d17810.3%
 
u17310.3%
 
y14730.2%
 
m14460.2%
 
g9870.2%
 
w8700.1%
 
k8080.1%
 
p6030.1%
 
j5260.1%
 
v4670.1%
 
c4480.1%
 
b3890.1%
 
x80< 0.1%
 
f74< 0.1%
 
z60< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S335813.1%
 
A21838.5%
 
R21768.5%
 
M21708.5%
 
K19967.8%
 
P18717.3%
 
D15876.2%
 
J12745.0%
 
V10974.3%
 
G10864.3%
 
B10804.2%
 
C10524.1%
 
H8853.5%
 
T7683.0%
 
L5272.1%
 
W5132.0%
 
N4851.9%
 
Y4091.6%
 
F2220.9%
 
U2210.9%
 
E2050.8%
 
I1780.7%
 
O1190.5%
 
Q610.2%
 
Z190.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
9481100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-23100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin62813998.5%
 
Common95041.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n37263159.3%
 
a19398830.9%
 
i40440.6%
 
h39680.6%
 
r37170.6%
 
e34120.5%
 
S33580.5%
 
l30390.5%
 
A21830.3%
 
R21760.3%
 
M21700.3%
 
s21100.3%
 
K19960.3%
 
t19820.3%
 
o19190.3%
 
P18710.3%
 
d17810.3%
 
u17310.3%
 
D15870.3%
 
y14730.2%
 
m14460.2%
 
J12740.2%
 
V10970.2%
 
G10860.2%
 
B10800.2%
 
Other values (27)110201.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
948199.8%
 
-230.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII637643100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n37263158.4%
 
a19398830.4%
 
94811.5%
 
i40440.6%
 
h39680.6%
 
r37170.6%
 
e34120.5%
 
S33580.5%
 
l30390.5%
 
A21830.3%
 
R21760.3%
 
M21700.3%
 
s21100.3%
 
K19960.3%
 
t19820.3%
 
o19190.3%
 
P18710.3%
 
d17810.3%
 
u17310.3%
 
D15870.2%
 
y14730.2%
 
m14460.2%
 
J12740.2%
 
V10970.2%
 
G10860.2%
 
Other values (29)121231.9%
 

dismissal_kind
Categorical

MISSING

Distinct9
Distinct (%)0.1%
Missing184357
Missing (%)95.3%
Memory size1.5 MiB
caught
5393 
bowled
1710 
run out
856 
lbw
572 
stumped
 
279
Other values (4)
 
237
ValueCountFrequency (%) 
caught53932.8%
 
bowled17100.9%
 
run out8560.4%
 
lbw5720.3%
 
stumped2790.1%
 
caught and bowled2110.1%
 
retired hurt12< 0.1%
 
hit wicket12< 0.1%
 
obstructing the field2< 0.1%
 
(Missing)18435795.3%
 
2020-12-21T12:37:28.639644image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-21T12:37:28.834422image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:29.289337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length3
Mean length3.150105479
Min length3

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n36978360.7%
 
a19017231.2%
 
u76091.2%
 
t67931.1%
 
h56300.9%
 
c56180.9%
 
g56060.9%
 
o27790.5%
 
w25050.4%
 
b24950.4%
 
l24950.4%
 
d24250.4%
 
e22400.4%
 
13060.2%
 
r8940.1%
 
s281< 0.1%
 
m279< 0.1%
 
p279< 0.1%
 
i40< 0.1%
 
k12< 0.1%
 
f2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter60793799.8%
 
Space Separator13060.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n36978360.8%
 
a19017231.3%
 
u76091.3%
 
t67931.1%
 
h56300.9%
 
c56180.9%
 
g56060.9%
 
o27790.5%
 
w25050.4%
 
b24950.4%
 
l24950.4%
 
d24250.4%
 
e22400.4%
 
r8940.1%
 
s281< 0.1%
 
m279< 0.1%
 
p279< 0.1%
 
i40< 0.1%
 
k12< 0.1%
 
f2< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1306100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin60793799.8%
 
Common13060.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n36978360.8%
 
a19017231.3%
 
u76091.3%
 
t67931.1%
 
h56300.9%
 
c56180.9%
 
g56060.9%
 
o27790.5%
 
w25050.4%
 
b24950.4%
 
l24950.4%
 
d24250.4%
 
e22400.4%
 
r8940.1%
 
s281< 0.1%
 
m279< 0.1%
 
p279< 0.1%
 
i40< 0.1%
 
k12< 0.1%
 
f2< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
1306100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII609243100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n36978360.7%
 
a19017231.2%
 
u76091.2%
 
t67931.1%
 
h56300.9%
 
c56180.9%
 
g56060.9%
 
o27790.5%
 
w25050.4%
 
b24950.4%
 
l24950.4%
 
d24250.4%
 
e22400.4%
 
13060.2%
 
r8940.1%
 
s281< 0.1%
 
m279< 0.1%
 
p279< 0.1%
 
i40< 0.1%
 
k12< 0.1%
 
f2< 0.1%
 

fielder
Categorical

HIGH CARDINALITY
MISSING

Distinct507
Distinct (%)7.8%
Missing186906
Missing (%)96.6%
Memory size1.5 MiB
MS Dhoni
 
159
KD Karthik
 
152
RV Uthappa
 
125
AB de Villiers
 
117
SK Raina
 
115
Other values (502)
5830 
ValueCountFrequency (%) 
MS Dhoni1590.1%
 
KD Karthik1520.1%
 
RV Uthappa1250.1%
 
AB de Villiers1170.1%
 
SK Raina1150.1%
 
PA Patel970.1%
 
RG Sharma93< 0.1%
 
V Kohli90< 0.1%
 
KA Pollard85< 0.1%
 
WP Saha84< 0.1%
 
NV Ojha82< 0.1%
 
RA Jadeja80< 0.1%
 
MK Pandey78< 0.1%
 
DJ Bravo78< 0.1%
 
AC Gilchrist75< 0.1%
 
S Dhawan74< 0.1%
 
AM Rahane66< 0.1%
 
AT Rayudu65< 0.1%
 
DA Warner64< 0.1%
 
KC Sangakkara58< 0.1%
 
SV Samson58< 0.1%
 
DA Miller53< 0.1%
 
SPD Smith51< 0.1%
 
YK Pathan51< 0.1%
 
BB McCullum50< 0.1%
 
Other values (482)43982.3%
 
(Missing)18690696.6%
 
2020-12-21T12:37:30.319634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique97 ?
Unique (%)1.5%
2020-12-21T12:37:30.699519image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length3
Mean length3.217193026
Min length3

Overview of Unicode Properties

Unique unicode characters55
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n37656160.5%
 
a19396831.2%
 
69311.1%
 
i30590.5%
 
h29950.5%
 
r26960.4%
 
e24430.4%
 
S23680.4%
 
l21730.3%
 
K16040.3%
 
M15680.3%
 
t15630.3%
 
A15420.2%
 
s15270.2%
 
R14960.2%
 
o14410.2%
 
P13810.2%
 
d13750.2%
 
u12520.2%
 
D12440.2%
 
m9710.2%
 
J9170.1%
 
B8480.1%
 
y8420.1%
 
V7950.1%
 
Other values (30)86581.4%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter59686995.9%
 
Uppercase Letter182542.9%
 
Space Separator69311.1%
 
Open Punctuation76< 0.1%
 
Close Punctuation76< 0.1%
 
Dash Punctuation12< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n37656163.1%
 
a19396832.5%
 
i30590.5%
 
h29950.5%
 
r26960.5%
 
e24430.4%
 
l21730.4%
 
t15630.3%
 
s15270.3%
 
o14410.2%
 
d13750.2%
 
u12520.2%
 
m9710.2%
 
y8420.1%
 
k7480.1%
 
g6280.1%
 
w5920.1%
 
p4390.1%
 
j3900.1%
 
v3860.1%
 
b3270.1%
 
c3160.1%
 
f57< 0.1%
 
q44< 0.1%
 
z40< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S236813.0%
 
K16048.8%
 
M15688.6%
 
A15428.4%
 
R14968.2%
 
P13817.6%
 
D12446.8%
 
J9175.0%
 
B8484.6%
 
V7954.4%
 
C6603.6%
 
G5923.2%
 
H5813.2%
 
T5473.0%
 
W3642.0%
 
N3411.9%
 
L3241.8%
 
Y2961.6%
 
U2071.1%
 
F1380.8%
 
I1320.7%
 
E1200.7%
 
O1180.6%
 
Q460.3%
 
Z250.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6931100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(76100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)76100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-12100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin61512398.9%
 
Common70951.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n37656161.2%
 
a19396831.5%
 
i30590.5%
 
h29950.5%
 
r26960.4%
 
e24430.4%
 
S23680.4%
 
l21730.4%
 
K16040.3%
 
M15680.3%
 
t15630.3%
 
A15420.3%
 
s15270.2%
 
R14960.2%
 
o14410.2%
 
P13810.2%
 
d13750.2%
 
u12520.2%
 
D12440.2%
 
m9710.2%
 
J9170.1%
 
B8480.1%
 
y8420.1%
 
V7950.1%
 
k7480.1%
 
Other values (26)77461.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
693197.7%
 
(761.1%
 
)761.1%
 
-120.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII622218100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n37656160.5%
 
a19396831.2%
 
69311.1%
 
i30590.5%
 
h29950.5%
 
r26960.4%
 
e24430.4%
 
S23680.4%
 
l21730.3%
 
K16040.3%
 
M15680.3%
 
t15630.3%
 
A15420.2%
 
s15270.2%
 
R14960.2%
 
o14410.2%
 
P13810.2%
 
d13750.2%
 
u12520.2%
 
D12440.2%
 
m9710.2%
 
J9170.1%
 
B8480.1%
 
y8420.1%
 
V7950.1%
 
Other values (30)86581.4%
 

Interactions

2020-12-21T12:36:19.519431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:19.949466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:20.409440image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:20.844418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:21.239752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:21.629612image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:22.044636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:22.439615image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:22.874674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:23.289541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:23.699508image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:24.099673image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:24.529546image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:24.985131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:25.444769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:25.859483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:26.259374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:26.699463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:27.109419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:27.559667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:27.979594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:28.414522image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:28.859403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:29.279496image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:29.719734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:30.149389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:30.569723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:30.959665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:31.384809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:31.779730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:32.234429image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:32.739634image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:33.264329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:33.699724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:34.069438image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:34.479781image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:35.259459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:35.629646image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:35.979467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:36.369597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:36.739439image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:37.119444image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:37.509650image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:37.909678image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:38.279648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:38.669613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:39.079724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:39.499642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:39.859810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:40.244326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:40.674523image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:41.029683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:41.449682image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:41.839458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:42.239608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:42.619730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:43.039735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:43.479749image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:43.919732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:44.334591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:44.739611image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:45.169726image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:45.600006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:46.029597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:46.439530image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:46.879462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:47.300672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:47.694653image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:48.102032image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:48.609625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:49.059458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:49.494458image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:49.890161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:50.269742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:50.689431image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:51.069526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:51.469322image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:51.849671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:52.249505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:52.699296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:53.149463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:53.544479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:53.944520image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:54.364594image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:54.749628image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:55.149397image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:55.609474image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:56.019516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:56.429643image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:56.849339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:57.299325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:57.734719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:58.114630image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:58.519349image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:58.949369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:59.349417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:36:59.779372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:00.199427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:00.659608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:01.059533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:01.469636image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:01.909567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:02.759549image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:03.129592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:03.584538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:04.019354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:04.459459image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:04.964705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:05.517271image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:05.969368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:06.389400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:06.784386image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:07.249365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:07.689344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:08.054457image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:08.439564image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:08.839468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:09.239187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:09.669699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:10.054483image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:10.499381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-12-21T12:37:30.999675image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-21T12:37:31.559264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-21T12:37:32.108705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-21T12:37:32.669725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-21T12:37:33.229642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-21T12:37:11.899724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:13.529412image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:15.209433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-12-21T12:37:15.859762image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
011Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills000000000NaNNaNNaN
111Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills000000000NaNNaNNaN
211Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills000000404NaNNaNNaN
311Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills000000000NaNNaNNaN
411Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills020000022NaNNaNNaN
511Sunrisers HyderabadRoyal Challengers Bangalore16S DhawanDA WarnerTS Mills000000000NaNNaNNaN
611Sunrisers HyderabadRoyal Challengers Bangalore17S DhawanDA WarnerTS Mills000100011NaNNaNNaN
711Sunrisers HyderabadRoyal Challengers Bangalore21S DhawanDA WarnerA Choudhary000000101NaNNaNNaN
811Sunrisers HyderabadRoyal Challengers Bangalore22DA WarnerS DhawanA Choudhary000000404NaNNaNNaN
911Sunrisers HyderabadRoyal Challengers Bangalore23DA WarnerS DhawanA Choudhary000010011NaNNaNNaN

Last rows

match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_overwide_runsbye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
19339412371812MIDC181KA PollardIshan KishanK Rabada000000000KA PollardbowledNaN
19339512371812MIDC182HH PandyaIshan KishanK Rabada000000101NaNNaNNaN
19339612371812MIDC183Ishan KishanHH PandyaK Rabada000000404NaNNaNNaN
19339712371812MIDC184Ishan KishanHH PandyaK Rabada000000101NaNNaNNaN
19339812371812MIDC185HH PandyaIshan KishanK Rabada000000000NaNNaNNaN
19339912371812MIDC186HH PandyaIshan KishanK Rabada000000101NaNNaNNaN
19340012371812MIDC191HH PandyaIshan KishanAnrich Nortje000000101NaNNaNNaN
19340112371812MIDC192Ishan KishanHH PandyaAnrich Nortje000000101NaNNaNNaN
19340212371812MIDC193HH PandyaIshan KishanAnrich Nortje000000000HH PandyacaughtAM Rahane
19340312371812MIDC194KH PandyaIshan KishanAnrich Nortje000000101NaNNaNNaN